The estimation of remaining useful life (“RUL”) of a faulty component is at the center of system prognostics and health management. RUL analysis provides operators with a potent tool for decision making, by quantifying how much time is left until functionality of the component is lost. This is especially important for systems, where unanticipated sub-system or component failure may lead to failure of the system, as a whole, which in turn may adversely affect safety of operations and/or may have costly consequences, resulting from emergency procedures implemented, delayed operations, unanticipated maintenance and repair, unserved or unmet obligations, and penalties. In situations where the cost/benefit analysis of using physics-based damage progression algorithms is not favorable but sufficient test data are available to comprehensively describe the damage space, one can employ data-driven approaches, or a combination of data-driven approaches and model-based (hybrid) techniques. Conventional data driven approaches attempt to either learn RUL directly from sensor measurements and features or by correlating trends in measurements or features to remaining life. These methods are frequently susceptible to artifacts in training data, as well as unanticipated future operating conditions.
Common to data-driven approaches is the modeling of desired system output (but not necessarily of the mechanics of the system) using historical data. Such approaches encompass “conventional” numerical algorithms, like linear regression or Kalman filters, as well as algorithms that are commonly found in the machine learning and data mining communities. The latter algorithms include neural networks, decision trees, and Support Vector Machines.
One of the most popular data-driven approaches in prognostics is artificial neural networks (“NNs”). An artificial neural network is a type of (typically nonlinear) model that establishes a set of interconnected functional relationships between input stimuli and desired output where the parameters of the functional relationship need to be adjusted for optimal performance. In addition to supervised networks, other types, such as reinforcement learning, have been proposed. Some of the conventional numerical techniques used for data-driven prognostics include wavelets, Kalman filters, particle filters, regression, demodulation and statistical methods. Another popular technique that is used for prognostics is fuzzy logic. Fuzzy logic provides a language (with syntax and local semantics) into which one can translate qualitative knowledge about the problem to be solved. The fuzzy reasoning mechanism has powerful interpolation properties that in turn give fuzzy logic a remarkable robustness with respect to variations in the system's parameters, disturbances, etc.
Evaluating RUL by correlating data with remaining life is a difficult problem because of the difficulty in recognizing relevant changes over both long term and short term horizons, so that the prediction is made with high accuracy and precision. Minimizing performance error is hard, because errors tend to be multiplicative, rather than additive.
Another core issue encountered in making a meaningful prediction is to account for and to subsequently bound the various kinds of uncertainties arising from different sources, such as process noise, measurement noise, inaccurate process models, etc. Long-term prediction of the time to failure involves large-grain uncertainties that must be represented effectively and managed efficiently. For example, as more information about past damage propagation and about future use becomes available, the uncertainty bounds should become narrowed. Therefore, it is critical to choose methods that can take care of these issues in addition to providing damage trajectories.
What is needed is a method that makes the prediction problem tractable, reduces performance error, and allows judicious management of the effects of uncertainty mentioned above.